class_docstr: Bottleneck block for ResNeSt.
  
  Args:
      inplane (int): Input planes of this block.
      planes (int): Middle planes of this block.
      groups (int): Groups of conv2.
      base_width (int): Base of width in terms of base channels. Default: 4.
      base_channels (int): Base of channels for calculating width.
          Default: 64.
      radix (int): Radix of SpltAtConv2d. Default: 2
      reduction_factor (int): Reduction factor of inter_channels in
          SplitAttentionConv2d. Default: 4.
      avg_down_stride (bool): Whether to use average pool for stride in
          Bottleneck. Default: True.
      kwargs (dict): Key word arguments for base class.
code: def forward(self, x):
      def _inner_forward(x):
          identity = x
  
          out = self.conv1(x)
          out = self.norm1(out)
          out = self.relu(out)
  
          if self.with_plugins:
              out = self.forward_plugin(
                  out, self.after_conv1_plugin_names
              )
  
          out = self.conv2(out)
  
          if self.avg_down_stride:
              out = self.avd_layer(out)
  
          if self.with_plugins:
              out = self.forward_plugin(
                  out, self.after_conv2_plugin_names
              )
  
          out = self.conv3(out)
          out = self.norm3(out)
  
          if self.with_plugins:
              out = self.forward_plugin(
                  out, self.after_conv3_plugin_names
              )
  
          if self.downsample is not None:
              identity = self.downsample(x)
  
          out += identity
  
          return out
  
      if self.with_cp and x.requires_grad:
          out = cp.checkpoint(_inner_forward, x)
      else:
          out = _inner_forward(x)
  
      out = self.relu(out)
  
      return out
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn import build_conv_layer, build_norm_layer
  from mmengine.model import BaseModule
  from mmdet.registry import MODELS
  from ..layers import ResLayer
  from .resnet import Bottleneck
  from .resnet import ResNetV1d
label_desc: Use image node_id 2 for calling the Bottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: out, out